In the US, Alzheimer?s disease (AD) is the single most expensive disease, the only disease in the top six for which the number of deaths is increasing. The greatest cost contributors are frequent hospitalizations, where falls are the largest culprit, and frequent need for assistance with the activities of daily living. A fall safety system shows the potential to reduce costs and increase quality of care by reducing the likelihood of emergency events (e.g., detecting falls before a fracture occurs and reducing the number of repeat falls). Unfortunately, current safety devices require wearable or sensor technology not suitable for individuals with dementia and incapable of showing caregivers how falls occur. Our goal is to deploy and demonstrate NestSense (also known as SafelyYou), an online fall detection system with off-the-shelf wall-mounted cameras to passively detect falls for patients with AD and related dementias (ADRD), enabled by a human-in-the-loop (HIL). The HIL will confirm the fall detection alerts provided by our artificial intelligence algorithms. We will demonstrate it for 100 patients in 13 memory care facilities. Memory care facilities can select parameters that matter for specific patients; for ex., some patients wake up frequently during the night while others should be assisted when they attempt to leave the bed at night. It does not require action of individuals / caregivers such as wearing a fall pendant and is therefore well-suited for individuals with ADRD. We leverage our HIL paradigm, in which our deep learning (a subfield of artificial intelligence) approaches identify and pre-filter falls well enough to leave the last check to a human, who will call the facilities in case of detected safety critical events (falls). The human can monitor several facilities at a given time. This project leverages the already recruited 100 patients in our partner 13 memory care facilities, recruited through our previous (IRB approved) pilot. The work will leverage our previous three pilots. ? Pilot 1: We demonstrated the feasibility of the system by collecting a proof-of-concept data containing 200 acted falls of healthy subjects and showed accurate fall detection. ? Pilot 2: We demonstrated acceptance of privacy/safety tradeoffs by patients, family and staff, through the collection of 3 months of video data at WindChime of Marin, a memory care facility from the Integral Senior Living network, in which we identified 4 total hours of fall data. This led to clinical benefits including a reduction of falls from 13 and 11 in the first 2 months to 2 in the final month, due to video review with care staff. ? Pilot 3 (ongoing): We demonstrated scalability and further acceptance by deploying the system in 13 facilities of the Carlton, Integral Senior Living, Pacifica and SRG networks, totaling 100 patients already monitored by our system (offline). The pilot proposed for this SBIR Phase I will translate the 100 cameras in these facilities into a real-time fall detection system which will run online for 3 months with a 24/7 HIL support. Compared to a 3-month baseline from the 100 cameras recording with the detection offline, we hypothesize this real-time detection system will lead to a statistically significant reduction in time on the ground after a fall, fall related hospitalizations, and length of hospital stay following fall incidents based on results described in previous clinical trials with 150+ participants [8,14].